切蛋糕：通过微秒级学习实现自适应执行引擎
Piece of CAKE: Adaptive Execution Engines via Microsecond-Scale Learning
AI总结：
CAKE通过微秒级学习实现自适应执行引擎，利用反事实反馈降低延迟，提升数据库性能
摘要：
低级数据库操作符通常允许多种物理实现（
英文摘要：
Low-level database operators often admit multiple physical implementations ("kernels") that are semantically equivalent but have vastly different performance characteristics depending on the input data distribution. Existing database systems typically rely on static heuristics or worst-case optimal defaults to select these kernels, often missing significant performance opportunities. In this work, we propose CAKE (Counterfactual Adaptive Kernel Execution), a system that learns to select the optimal kernel for each data "morsel" using a microsecond-scale contextual multi-armed bandit. CAKE circumvents the high latency of traditional reinforcement learning by exploiting the cheapness of counterfactuals -- selectively running multiple kernels to obtain full feedback -- and compiling policies into low-latency regret trees. Experimentally, we show that CAKE can reduce end-to-end workload latency by up to 2x compared to state-of-the-art static heuristics.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
